Firm-Tuned Contract AI (the US, 2026)

Most AI contract tools produce a competent, generic contract. That sounds fine until you read the output: it uses terms your company would never accept, defines parties the wrong way, hedges where your playbook says to push, and reads nothing like the agreements your team actually signs. A generic draft is a starting point you still have to rewrite. A firm-tuned draft is one you can send. This page explains why generic AI misses your standards, what firm-tuned means in practice, and how Adira builds a model of your company so its drafts come out in your favor and in your voice. ## Why generic AI contract tools miss your standards An AI model trained on the open world knows what a contract looks like in general. It does not know your company. It does not know that you always cap liability at fees paid in the prior twelve months, that your non-competes run twelve months not twenty-four, that you never accept an uncapped indemnity, or that your legal team has a house style for definitions and numbering. So it gives you the average of everything it has seen, which is a contract written for no one in particular. That gap shows up in three expensive ways. First, every draft needs heavy rework to match your positions, so the time you saved generating it comes straight back in editing. Second, the output skews neutral, which in a negotiation means it quietly gives away ground your playbook would have held. Third, it is inconsistent: ask twice and you get two different contracts, so your paper stops looking like your paper. For a legal team, consistency is not a nicety. It is how you manage risk at scale. A concrete example makes the gap obvious. Suppose your standard is a liability cap at fees paid in the prior twelve months, a mutual indemnity, and a thirty-day cure period before termination. Ask a generic tool for a services agreement and you are as likely to get an uncapped liability clause, a one-sided indemnity running against you, and no cure period at all, not because the tool is bad, but because that is a common shape in the world it learned from. Now your lawyer has to catch all three, rewrite them, and hope the next draft does not reintroduce them. Multiply that across every contract type and every drafter on your team, and the cost of generic output is no longer abstract. ## What firm-tuned actually means Firm-tuned does not mean retraining a giant model on your files. It means the tool holds a working model of your company and injects it into everything it drafts and reviews. A good firm-tuned system knows: - What you do and how you contract: your business, your common deal types, your counterparties.

• Your preferred terms: the positions you take by default on liability, indemnity, term, termination, IP and the rest.
• Your risk posture: where you push hard and where you concede.
• Your house style: how you define terms, number clauses and phrase standard language. With that in hand, the AI stops writing an average contract and starts writing yours. The draft already reflects your positions, so review is about checking, not rebuilding. ## How Adira does it: Company Persona Adira builds this through a module called Company Persona. It constructs an in-depth profile of your company from your own documents, with optional public research: what the company does, who its directors are, how and where it contracts, its preferred terms, its risk posture and its house style. That persona is then injected into every draft, so contracts sound like your company and skew terms in your favor rather than landing in a neutral middle. Two things make this honest and usable. The persona is fully viewable and editable, so it is not a black box: you can see what Adira believes about your company and correct it. And because Adira models each contract as a structured clause tree rather than flat text, your preferred positions attach to real clauses, so they carry through drafting, plain-English editing and review consistently. Importantly, learning your documents is not the same as training a shared model on them. Adira does not train on your contract corpus; your documents inform your persona, under your control. ## Firm-tuned versus the alternatives It is worth being fair about the other options. A general chatbot can be coached with a long prompt describing your standards, and that helps for one document, but nothing is retained: you paste the same instructions every time, and there is no shared team memory. Template libraries encode your standards once but are static, so they age and cannot reason about a new counterparty draft. Mature CLM platforms such as Ironclad and Icertis offer clause libraries and playbooks that larger enterprises run well, and for a big legal operation those are strong, deeply integrated systems. Adira's difference is that the company model is built from your own documents by a frontier AI and applied automatically across drafting and review, with public pricing that lets a smaller team adopt it without a procurement cycle. That makes it a strong fit for startups and lean in-house teams, not a claim to outrank the incumbents on enterprise breadth. ## What to check before you trust a firm-tuned claim Many tools say they personalize. Ask three concrete questions. Can I see and edit what the tool has learned about my company, or is it hidden? Does my data train a shared model, or stay mine? And does the tuning carry through editing and review, or only the first draft? A credible firm-tuned tool answers all three cleanly, and a vague answer to any of them usually means the personalization is shallower than the marketing suggests. Adira makes the persona visible and editable, does not train on your corpus, and carries your positions through the whole clause tree. You can see the product at Adira. ## Bottom line Generic AI gives you the average contract, which is the one contract your company does not want. Firm-tuned AI holds a model of how you contract, your preferred terms and your house style, and writes drafts that already reflect them. Adira does this with Company Persona, built from your own documents, fully editable, applied across a structured clause tree, and kept private because Adira does not train on your corpus. The result is less rewriting, more consistent paper, and drafts that read like yours rather than like everyone's.